Published November 15, 2024
| Version v1
Journal article
Open
Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features
Creators
- 1. University of Chicago
- 2. Geisinger Cancer Institute
- 3. SimBioSys
- 4. University of North Carolina at Chapel Hill
- 5. University of Pennsylvania Health System
- 6. NorthShore University HealthSystem
Description
Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of molecular features. These approaches distill cancer histologic images into high-level features, which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network—HistoXGAN—capable of reconstructing representative histology using feature vectors produced by common feature extractors. We evaluate HistoXGAN across 29 cancer subtypes and demonstrate that reconstructed images retain information regarding tumor grade, histologic subtype, and gene expression patterns. We leverage HistoXGAN to illustrate the underlying histologic features for deep learning models for actionable mutations, identify model reliance on histologic batch effect in predictions, and demonstrate accurate reconstruction of tumor histology from radiographic imaging for a "virtual biopsy."
Data availability
All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Data from TCGA including digital histology and most of the clinical annotations used are available from https://portal.gdc.cancer.gov/ and https://cbioportal.org, and CPTAC images are available from https://wiki.cancerimagingarchive.net/display/Public/CPTAC+Pathology+Slide+Downloads. Annotations for HRD status are available in the published work of Knijnenburg et al. (47), and annotations for genomic ancestry were obtained from Carrot-Zhang et al. (46). Codes used for this analysis, trained models, and matched radiomic features/histology features from the UCMC validation dataset to replicate this analysis are available at https://doi.org/10.5281/zenodo.13785423; code is also available at https://github.com/fmhoward/HistoXGAN. Additional digital images can also be provided pending scientific review and a completed data use agreement. Requests for digital images should be submitted to F.M.H. (frederick.howard@uchospitals.edu). Licensing of code and data is through CC BY-NC 4.0.
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Additional details
Identifiers
- DOI
- 10.1126/sciadv.adq0856
- Other
- oai:uchicago.tind.io:14030
Funding
- National Institutes of Health
- R56DE030958
- U.S. Department of Defense
- BC211095P1
- American Cancer Society
- National Cancer Institute
- K08CA283261
- National Cancer Institute
- R01CA276652
- National Cancer Institute
- P50-CA058223
- Cancer Research Foundation
- Lynn Sage Breast Cancer Foundation
- ASCRS Research Foundation
- BCRF-23-127
- Stand Up To Cancer
- Horizon Therapeutics
- 2021-SC1-BHC
- Adenoid Cystic Carcinoma Research Foundation